Ship instance segmentation technology is becoming more and more important in applications such as ship identification, monitoring and tracking, which is of great significance for maritime safety management. However, due to the different shapes of ships, as well as the complexity and volatility of illumination, weather and other factors, the existing ship instance segmentation methods are often difficult to achieve good segmentation results. To address this problem, this paper proposes a real-time segmentation method for ship instances based on contours that uses CenterNet algorithm to detect ship targets. The core network uses DLA-34 (Deep Layer Aggregation) to ensure the detection accuracy and speed. Then, the Deep Snake method is used to segment the ship object accurately. In order to verify the effectiveness of the proposed algorithm, this study constructed a dedicated dataset of 2300 images involving complex environments such as inland rivers and ports under three typical conditions: day, night, and haze. In addition, this dataset was used to test the proposed method, and the average recall (AR) rate was 95.5% and the average precision (AP) rate was 93.1%. The proposed method can achieve the inference performance of 47 frames per second on the RTX3090 GPU.